| /* |
| * Copyright (c) 2018 ARM Limited. |
| * |
| * SPDX-License-Identifier: MIT |
| * |
| * Permission is hereby granted, free of charge, to any person obtaining a copy |
| * of this software and associated documentation files (the "Software"), to |
| * deal in the Software without restriction, including without limitation the |
| * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or |
| * sell copies of the Software, and to permit persons to whom the Software is |
| * furnished to do so, subject to the following conditions: |
| * |
| * The above copyright notice and this permission notice shall be included in all |
| * copies or substantial portions of the Software. |
| * |
| * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR |
| * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, |
| * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE |
| * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER |
| * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, |
| * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE |
| * SOFTWARE. |
| */ |
| #include "arm_compute/runtime/CL/functions/CLWidthConcatenateLayer.h" |
| |
| #include "arm_compute/core/CL/ICLTensor.h" |
| #include "arm_compute/core/Error.h" |
| #include "arm_compute/core/Helpers.h" |
| #include "arm_compute/core/TensorInfo.h" |
| #include "arm_compute/core/Types.h" |
| #include "arm_compute/core/utils/misc/ShapeCalculator.h" |
| #include "arm_compute/runtime/CL/CLScheduler.h" |
| #include "support/ToolchainSupport.h" |
| |
| using namespace arm_compute; |
| |
| CLWidthConcatenateLayer::CLWidthConcatenateLayer() // NOLINT |
| : _concat_kernels_vector(), |
| _num_inputs(0) |
| { |
| } |
| |
| Status CLWidthConcatenateLayer::validate(const std::vector<ITensorInfo *> &inputs_vector, const ITensorInfo *output) // NOLINT |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(output); |
| ARM_COMPUTE_RETURN_ERROR_ON(inputs_vector.size() < 2); |
| |
| // Output auto inizialitation if not yet initialized |
| TensorInfo tmp_output_info = *output->clone(); |
| TensorShape output_shape = arm_compute::misc::shape_calculator::calculate_width_concatenate_shape(inputs_vector); |
| auto_init_if_empty(tmp_output_info, output_shape, 1, inputs_vector[0]->data_type(), inputs_vector[0]->fixed_point_position()); |
| |
| unsigned int width_offset = 0; |
| for(const auto &input : inputs_vector) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input); |
| ARM_COMPUTE_RETURN_ON_ERROR(CLWidthConcatenateLayerKernel::validate(input, width_offset, &tmp_output_info)); |
| width_offset += input->dimension(0); |
| } |
| |
| return Status{}; |
| } |
| |
| void CLWidthConcatenateLayer::configure(std::vector<ICLTensor *> inputs_vector, ICLTensor *output) // NOLINT |
| { |
| _num_inputs = inputs_vector.size(); |
| |
| std::vector<ITensorInfo *> inputs_vector_info; |
| for(unsigned int i = 0; i < _num_inputs; i++) |
| { |
| inputs_vector_info.emplace_back(inputs_vector.at(i)->info()); |
| } |
| TensorShape output_shape = arm_compute::misc::shape_calculator::calculate_width_concatenate_shape(inputs_vector); |
| |
| // Output auto inizialitation if not yet initialized |
| auto_init_if_empty(*output->info(), output_shape, 1, inputs_vector[0]->info()->data_type(), inputs_vector[0]->info()->fixed_point_position()); |
| ARM_COMPUTE_ERROR_THROW_ON(CLWidthConcatenateLayer::validate(inputs_vector_info, output->info())); |
| |
| unsigned int width_offset = 0; |
| |
| _concat_kernels_vector = arm_compute::support::cpp14::make_unique<CLWidthConcatenateLayerKernel[]>(_num_inputs); |
| |
| for(unsigned int i = 0; i < _num_inputs; i++) |
| { |
| _concat_kernels_vector[i].configure(inputs_vector.at(i), width_offset, output); |
| width_offset += inputs_vector.at(i)->info()->dimension(0); |
| } |
| } |
| |
| void CLWidthConcatenateLayer::run() |
| { |
| cl::CommandQueue q = CLScheduler::get().queue(); |
| |
| for(unsigned i = 0; i < _num_inputs; i++) |
| { |
| CLScheduler::get().enqueue(_concat_kernels_vector[i], true); |
| } |
| } |